Dada2_Mars2020

---

1. IMPORT

---

library(dada2)
packageVersion("dada2") # check dada2 version

## [1] '1.22.0'

library(Biostrings)
library(ShortRead)
library(seqTools) # per base sequence content
library(phyloseq)
library(ggplot2)
library(data.table)
library(plyr)
library(dplyr)
library(qckitfastq) # per base sequence content
library(stringr)

# ROOT DIRECTORY (to modify on your computer)
path.root <- "~/Projects/MetaIBS"
path.mars <- file.path(path.root, "scripts/analysis-individual/Mars-2020")
path.data <- file.path(path.root, "data/analysis-individual/Mars-2020")

---

2. QUALITY CHECK

---

2.1. Fastq quality profiles

First, we import the fastq files containing the raw reads. The samples were downloaded from the SRA database with the accession number PRJEB37924. Only sigmoid biopsy samples that were analyzed by 16S rRNA sequencing were downloaded (see 00_Metadata-Mars).

# Save the path to the directory containing the fastq zipped files
path.fastq <- file.path(path.data, "raw_fastq")
# list.files(path.fastq) # check we are in the right directory

# fastq filenames have format: SAMPLENAME.fastq.gz
# Saves the whole directory path to each file name
fnFs <- sort(list.files(path.fastq, pattern="_1.fastq.gz", full.names = TRUE)) # FWD reads
fnRs <- sort(list.files(path.fastq, pattern="_2.fastq.gz", full.names = TRUE)) # REV reads
show(fnFs[1:5])
show(fnRs[1:5])

# Extract sample names, assuming filenames have format: SAMPLENAME.fastq.gz
sample.names <- sapply(strsplit(basename(fnFs), "_"), `[`, 1)
show(sample.names[1:5]) # saves only the file name (without the path)

# Look at quality of all files
for (i in 1:3){  # 1:length(fnFs)
  show(plotQualityProfile(fnFs[i]))
  show(plotQualityProfile(fnRs[i]))
}

# Look at nb of reads per sample
# raw_stats <- data.frame('sample' = sample.names,
#                         'reads' = fastqq(fnFs)@nReads)
# min(raw_stats$reads)
# max(raw_stats$reads)
# mean(raw_stats$reads)

We will have a quick peak at the per base sequence content of the reads in some samples, to make sure there is no anomaly (i.e. all reads having the same sequence).

# Look at per base sequence content (forward read)
fseqF1 <- seqTools::fastqq(fnFs[10])

## [fastqq] File ( 1/1) '/Users/enigma/Projects/MetaIBS/data/analysis-individual/Mars-2020/raw_fastq/ERR5169592_1.fastq.gz' done.

rcF1 <- read_content(fseqF1)
plot_read_content(rcF1) + labs(title = "Per base sequence content - Forward read")

fseqR1 <- seqTools::fastqq(fnRs[10])

## [fastqq] File ( 1/1) '/Users/enigma/Projects/MetaIBS/data/analysis-individual/Mars-2020/raw_fastq/ERR5169592_2.fastq.gz' done.

rcR1 <- read_content(fseqR1)
plot_read_content(rcR1) + labs(title = "Per base sequence content - Reverse read")

2.2. Look for primers

Now, we will look whether the reads still contain the primers. The methods section indicates that the V4 region of the 16S rRNA gene was amplified as described in the paper by Gohl et al.. Primer sequences are shared in that paper.

# V4
FWD <- "GTGCCAGCMGCCGCGGTAA"  # F515 forward primer sequence
REV <- "GGACTACHVGGGTWTCTAAT" # R806 reverse primer sequence 

# Function that, from the primer sequence, will return all combinations possible (complement, reverse complement, ...)
allOrients <- function(primer) {
    # Create all orientations of the input sequence
    require(Biostrings)
    dna <- DNAString(primer)  # The Biostrings works w/ DNAString objects rather than character vectors
    orients <- c(Forward = dna, Complement = complement(dna), Reverse = reverse(dna), 
        RevComp = reverseComplement(dna))
    return(sapply(orients, toString))
}

# Get all combinations of the primer sequences
FWD.orients <- allOrients(FWD) # F515
REV.orients <- allOrients(REV) # R806
FWD.orients # sanity check

##               Forward            Complement               Reverse 
## "GTGCCAGCMGCCGCGGTAA" "CACGGTCGKCGGCGCCATT" "AATGGCGCCGMCGACCGTG" 
##               RevComp 
## "TTACCGCGGCKGCTGGCAC"

REV.orients

##                Forward             Complement                Reverse 
## "GGACTACHVGGGTWTCTAAT" "CCTGATGDBCCCAWAGATTA" "TAATCTWTGGGVHCATCAGG" 
##                RevComp 
## "ATTAGAWACCCBDGTAGTCC"

# Function that counts number of reads in which a sequence is found
primerHits <- function(primer, fn) {
    nhits <- vcountPattern(primer, sread(readFastq(fn)), fixed = FALSE, max.mismatch = 2)
    return(sum(nhits > 0))
}

# Get a table to know how many times the U515 and E786 primers are found in the reads of each sample
for (i in 1:3){
  cat("SAMPLE", sample.names[i], "with total number of", raw_stats[i,'reads'], "reads\n\n")
  x <- rbind(ForwardRead.FWDPrimer = sapply(FWD.orients, primerHits, fn = fnFs[[i]]),
             ForwardRead.REVPrimer = sapply(REV.orients, primerHits, fn = fnFs[[i]]),
             ReverseRead.FWDPrimer = sapply(FWD.orients, primerHits, fn = fnRs[[i]]), 
             ReverseRead.REVPrimer = sapply(REV.orients, primerHits, fn = fnRs[[i]]))
  print(x)
  cat("\n____________________________________________\n\n")
}

## SAMPLE ERR5169583 with total number of 105593 reads
## 
##                       Forward Complement Reverse RevComp
## ForwardRead.FWDPrimer  102879          0       0       4
## ForwardRead.REVPrimer       4          0       0   96739
## ReverseRead.FWDPrimer       4          0       0   83295
## ReverseRead.REVPrimer  103571          0       0       3
## 
## ____________________________________________
## 
## SAMPLE ERR5169584 with total number of 129697 reads
## 
##                       Forward Complement Reverse RevComp
## ForwardRead.FWDPrimer  125832          0       0      13
## ForwardRead.REVPrimer      13          0       0  118601
## ReverseRead.FWDPrimer      13          0       0  103070
## ReverseRead.REVPrimer  127174          0       0      13
## 
## ____________________________________________
## 
## SAMPLE ERR5169585 with total number of 70485 reads
## 
##                       Forward Complement Reverse RevComp
## ForwardRead.FWDPrimer   68860          0       0       1
## ForwardRead.REVPrimer       1          0       0   64299
## ReverseRead.FWDPrimer       1          0       0   55897
## ReverseRead.REVPrimer   69315          0       0       1
## 
## ____________________________________________

Let’s have a quick look at where primers are positioned in the forward/reverse reads

# Function that gets position in which sequence is found
primerHitsPosition <- function(primer, fn){
  hits <- as.data.frame(vmatchPattern(primer, sread(readFastq(fn)), fixed = FALSE, max.mismatch = 2))
  hits <- hits[,c("group", "start")]
  colnames(hits) <- c("sample", "start")
  hits$sample <- sapply(strsplit(basename(fn), "_"), `[`, 1)
  hits$readslength <- seqTools::fastqq(fn)@maxSeqLen
  return(hits)
}

# Get position of primers in forward reads
FWDpos <- data.frame()
for(i in 1:length(fnFs)){
  cat("SAMPLE", i)
  newF <- primerHitsPosition(FWD.orients["Forward"], fnFs[[i]])
  newF$primer <- "FWDprimer"
  newR <- primerHitsPosition(REV.orients["RevComp"], fnFs[[i]])
  newR$primer <- "REVprimer"
  FWDpos <- rbind(newF, newR, FWDpos)
}

# Get position of REV primers
REVpos <- data.frame()
for(i in 1:length(fnRs)){
  cat("SAMPLE", i)
  newF <- primerHitsPosition(FWD.orients["RevComp"], fnRs[[i]])
  newF$primer <- "FWDprimer"
  newR <- primerHitsPosition(REV.orients["Forward"], fnRs[[i]])
  newR$primer <- "REVprimer"
  REVpos <- rbind(newF, newR, REVpos)
}

ggplot(FWDpos, aes(x=start))+
  geom_density(aes(y=..scaled..)) +
  facet_wrap(~primer)+
  xlim(c(0,max(FWDpos$readslength)))+
  labs(x="start position of primer", y="proportion of primers starting at x position", title="FORWARD READS")

ggplot(REVpos, aes(x=start))+
  geom_density(aes(y=..scaled..)) +
  facet_wrap(~primer)+
  xlim(c(0,max(REVpos$readslength)))+
  labs(x="start position of primer", y="proportion of primers starting at x position", title="REVERSE READS")

---

3. FILTER AND TRIM

---

3.1. Primer removal

The reads indeed contain the primers (both FWD and REV primers). We will keep only reads containing primers, and then remove the primers!

# KEEP READS WITH PRIMER AND REMOVE PRIMER+BARCODE
# Place filtered files in a filtered1/ subdirectory
FWD.filt1_samples <- file.path(path.data, "filtered1", paste0(sample.names, "_1_filt.fastq.gz")) # FWD reads
REV.filt1_samples <- file.path(path.data, "filtered1", paste0(sample.names, "_2_filt.fastq.gz")) # REV reads
# Assign names for the filtered fastq.gz files
names(FWD.filt1_samples) <- sample.names
names(REV.filt1_samples) <- sample.names

# Keep only reads with primers & remove primers
FWD.out1 <- removePrimers(fn = fnFs, fout = FWD.filt1_samples,
                          primer.fwd = FWD.orients[["Forward"]],
                          trim.fwd = TRUE,
                          primer.rev=REV.orients[["RevComp"]],
                          trim.rev=TRUE,
                          orient = FALSE, # re-orient reads if needed
                          compress = TRUE, verbose = TRUE)
REV.out1 <- removePrimers(fn = fnRs, fout = REV.filt1_samples,
                          primer.fwd = REV.orients[["Forward"]],
                          trim.fwd = TRUE,
                          primer.rev = FWD.orients[["RevComp"]],
                          trim.rev=TRUE,
                          orient = FALSE, # re-orient reads if needed
                          compress = TRUE, verbose = TRUE)

# Primer removal
FWD.out1[1:3,]

##                       reads.in reads.out
## ERR5169583_1.fastq.gz   105593     94525
## ERR5169584_1.fastq.gz   129697    115395
## ERR5169585_1.fastq.gz    70485     62930

REV.out1[1:3,]

##                       reads.in reads.out
## ERR5169583_2.fastq.gz   105593     82479
## ERR5169584_2.fastq.gz   129697    101971
## ERR5169585_2.fastq.gz    70485     55320

# Quality profile after primer removal
for (i in 1:3){
  show(plotQualityProfile(FWD.filt1_samples[i]))
  show(plotQualityProfile(REV.filt1_samples[i]))
}

# Check primers were removed
for (i in 1:3){
  cat("SAMPLE ", sample.names[i], "\n")
  # Get a table to know how many times the 341F and 805R primers are found (in how many reads)
  x <- rbind(ForwardRead.FWDPrimer = sapply(FWD.orients, primerHits, fn = FWD.filt1_samples[[i]]),
             ForwardRead.REVPrimer = sapply(REV.orients, primerHits, fn = FWD.filt1_samples[[i]]),
             ReverseRead.FWDPrimer = sapply(FWD.orients, primerHits, fn = REV.filt1_samples[[i]]), 
             ReverseRead.REVPrimer = sapply(REV.orients, primerHits, fn = REV.filt1_samples[[i]]))
  print(x)
  # cat("\nTotal number of reads: ", fastqq(FWD.filt1_samples[i])@nReads)
  cat("\n____________________________________________\n\n")
}

## SAMPLE  ERR5169583 
##                       Forward Complement Reverse RevComp
## ForwardRead.FWDPrimer       0          0       0       0
## ForwardRead.REVPrimer       0          0       0       4
## ReverseRead.FWDPrimer       0          0       0      13
## ReverseRead.REVPrimer       0          0       0       0
## 
## ____________________________________________
## 
## SAMPLE  ERR5169584 
##                       Forward Complement Reverse RevComp
## ForwardRead.FWDPrimer       0          0       0       0
## ForwardRead.REVPrimer       0          0       0       4
## ReverseRead.FWDPrimer       0          0       0      12
## ReverseRead.REVPrimer       0          0       0       0
## 
## ____________________________________________
## 
## SAMPLE  ERR5169585 
##                       Forward Complement Reverse RevComp
## ForwardRead.FWDPrimer       0          0       0       0
## ForwardRead.REVPrimer       0          0       0       2
## ReverseRead.FWDPrimer       0          0       0       8
## ReverseRead.REVPrimer       0          0       0       0
## 
## ____________________________________________

3.2. Quality filtering

Then, we perform a quality filtering of the reads.

# Place filtered files in a filtered/ subdirectory
FWD.filt2_samples <- file.path(path.data, "filtered2", paste0(sample.names, "_F_filt2.fastq.gz")) # FWD reads
REV.filt2_samples <- file.path(path.data, "filtered2", paste0(sample.names, "_R_filt2.fastq.gz")) # REV reads
# Assign names for the filtered fastq.gz files
names(FWD.filt2_samples) <- sample.names
names(REV.filt2_samples) <- sample.names

# Filter
out2 <- filterAndTrim(fwd = FWD.filt1_samples, filt = FWD.filt2_samples,
                      rev = REV.filt1_samples, filt.rev = REV.filt2_samples,
                      maxEE=3, # reads with more than 3 expected errors (sum(10e(-Q/10))) are discarded
                      truncQ=10, # Truncate reads at the first instance of a quality score less than or equal to truncQ.
                      minLen=150, # Discard reads shorter than 150 bp.
                      matchIDs = TRUE,
                      id.sep="/",
                      compress=TRUE,
                      multithread = TRUE,
                      verbose=TRUE)

# IDs look like this:
# FWD read: @ERR5169583.1 M04141:159:000000000-BFDJP:1:1101:10000:14878/1
# REV read: @ERR5169583.1 M04141:159:000000000-BFDJP:1:1101:10000:14878/2
# So we want DADA2 to recognize the part before "/1" as an identifier => id.sep="/"

Let’s look at the output filtered fastq files as sanity check.

out2[1:6,] # show how many reads were filtered in each file

##                            reads.in reads.out
## ERR5169583_1_filt.fastq.gz    94525     60807
## ERR5169584_1_filt.fastq.gz   115395     76012
## ERR5169585_1_filt.fastq.gz    62930     41097
## ERR5169586_1_filt.fastq.gz    76871     50634
## ERR5169587_1_filt.fastq.gz    78826     52367
## ERR5169588_1_filt.fastq.gz    36336     16678

# Look at quality profile of all filtered files
for (i in 1:3){
  show(plotQualityProfile(FWD.filt2_samples[i]))
  show(plotQualityProfile(REV.filt2_samples[i]))
}

---

4. CONSTRUCT ASV TABLE

---

4.1. Learn error rates

Now we will build the parametric error model, to be able to infer amplicon sequence variants (ASVs) later on.

set.seed(123)
errF <- learnErrors(FWD.filt2_samples, multithread=TRUE, randomize=TRUE, verbose = 1)
set.seed(123)
errR <- learnErrors(REV.filt2_samples, multithread=TRUE, randomize=TRUE, verbose = 1)

The error rates for each possible transition (A→C, A→G, …) are shown. Points are the observed error rates for each consensus quality score. The black line shows the estimated error rates after convergence of the machine-learning algorithm. The red line shows the error rates expected under the nominal definition of the Q-score. Here the estimated error rates (black line) are a good fit to the observed rates (points), and the error rates drop with increased quality as expected.

plotErrors(errF, nominalQ = TRUE) # Forward reads

plotErrors(errR, nominalQ = TRUE) # Reverse reads

4.2. Infer sample composition

The dada() algorithm infers sequence variants based on estimated errors (previous step). Firstly, we de-replicate the reads in each sample, to reduce the computation time. De-replication is a common step in almost all modern ASV inference (or OTU picking) pipelines, but a unique feature of derepFastq is that it maintains a summary of the quality information for each dereplicated sequence in $quals.

# Dereplicate the reads in the sample
derepF <- derepFastq(FWD.filt2_samples) # forward
derepR <- derepFastq(REV.filt2_samples) # reverse

# Infer sequence variants
dadaFs <- dada(derepF, err=errF, multithread=TRUE) # forward
dadaRs <- dada(derepR, err=errR, multithread=TRUE) # reverse

# Inspect the infered sequence variants
for (i in 1:3){
  print(dadaFs[[i]])
  print(dadaRs[[i]])
  print("________________")
}

## dada-class: object describing DADA2 denoising results
## 195 sequence variants were inferred from 8729 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16
## dada-class: object describing DADA2 denoising results
## 123 sequence variants were inferred from 12341 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16
## [1] "________________"
## dada-class: object describing DADA2 denoising results
## 203 sequence variants were inferred from 10986 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16
## dada-class: object describing DADA2 denoising results
## 120 sequence variants were inferred from 14807 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16
## [1] "________________"
## dada-class: object describing DADA2 denoising results
## 168 sequence variants were inferred from 6836 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16
## dada-class: object describing DADA2 denoising results
## 107 sequence variants were inferred from 9596 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16
## [1] "________________"

4.3. Merge paired-end reads

We now need to merge paired reads.

mergers <- mergePairs(dadaFs, derepF, dadaRs, derepR, verbose=TRUE)

head(mergers[[1]])

4.4. Construct ASV table

We can now construct an amplicon sequence variant table (ASV) table, a higher-resolution version of the OTU table produced by traditional methods.

# Make sequence table from the infered sequence variants
seqtable <- makeSequenceTable(mergers)

# We should have 72 samples (72 rows)
dim(seqtable)

## [1]   72 1571

# Inspect distribution of sequence lengths
hist(nchar(getSequences(seqtable)), breaks = 100, xlab = "ASV length", ylab = "Number of ASVs", main="")

# Sequences should be between 515F - 806R, so around ~250bp (removing the primers lengths).
# Remove any sequence variant outside 200-300bp
seqtable.new <- seqtable[,nchar(colnames(seqtable)) %in% 200:300]
dim(seqtable.new)

## [1]   72 1548

hist(nchar(getSequences(seqtable.new)), breaks = 100, xlab = "ASV length", ylab = "Number of ASVs", main="")

# check we haven't lost too many counts
sum(seqtable.new)/sum(seqtable)

## [1] 0.6941104

4.5. Remove chimeras

The core dada method corrects substitution and indel errors, but chimeras remain. Fortunately, the accuracy of sequence variants after denoising makes identifying chimeric ASVs simpler than when dealing with fuzzy OTUs. Chimeric sequences are identified if they can be exactly reconstructed by combining a left-segment and a right-segment from two more abundant “parent” sequences.

seqtable.nochim <- removeBimeraDenovo(seqtable.new, method="consensus", multithread=TRUE, verbose=TRUE)

# Check how many sequence variants we have after removing chimeras
dim(seqtable.nochim)

## [1]   72 1529

# Check how many reads we have after removing chimeras (we should keep the vast majority of the reads, like > 80%)
sum(seqtable.nochim)/sum(seqtable.new)

## [1] 0.9982173

---

5. LOOK AT READS COUNT THROUGH PIPELINE

---

Sanity check before assigning taxonomy.

# Function that counts nb of reads
getN <- function(x) sum(getUniques(x))

# Table that will count number of reads for each process of interest (input reads, filtered reads, denoised reads, non chimera reads)
track <- cbind(FWD.out1,
           data.frame("primerfiltR"=REV.out1[,2]),
           data.frame("qualityfilt"=out2[,2]),
           sapply(dadaFs, getN),
           sapply(dadaRs, getN),
           sapply(mergers, getN),
           rowSums(seqtable.nochim),
           as.integer(rowSums(seqtable.nochim)*100/FWD.out1[,1]))

# Assign column and row names
colnames(track) <- c("input", "primerfiltF", "primerfiltR", "quality-filt", "denoisedF", "denoisedR", 'merged', 'nonchim', "%input->output")
rownames(track) <- sample.names

# Show final table: for each row/sample, we have shown the initial number of reads, filtered reads, denoised reads, and non chimera reads
track

---

6. TAXONOMIC TABLE

---

Extensions: The dada2 package also implements a method to make species level assignments based on exact matching between ASVs and sequenced reference strains. Recent analysis suggests that exact matching (or 100% identity) is the only appropriate way to assign species to 16S gene fragments. Currently, species-assignment training fastas are available for the Silva and RDP 16S databases. To follow the optional species addition step, download the silva_species_assignment_v132.fa.gz file, and place it in the directory with the fastq files.

path.silva <- file.path(path.root, "data/analysis-individual/CLUSTER/taxonomy/silva-taxonomic-ref")

# Assign taxonomy (with silva v138)
taxa <- assignTaxonomy(seqtable.nochim, file.path(path.silva, "silva_nr99_v138.1_train_set.fa.gz"),
                       tryRC = TRUE, # try reverse complement of the sequences
                       multithread=TRUE, verbose = TRUE)

# Add species assignment
taxa <- addSpecies(taxa, file.path(path.silva, "silva_species_assignment_v138.1.fa.gz"))

# Check how the taxonomy table looks like
taxa.print <- taxa 
rownames(taxa.print) <- NULL # Removing sequence rownames for display only
head(taxa.print)

##      Kingdom    Phylum         Class         Order            
## [1,] "Bacteria" "Bacteroidota" "Bacteroidia" "Bacteroidales"  
## [2,] "Bacteria" "Firmicutes"   "Clostridia"  "Lachnospirales" 
## [3,] "Bacteria" "Firmicutes"   "Clostridia"  "Oscillospirales"
## [4,] "Bacteria" "Firmicutes"   "Clostridia"  "Lachnospirales" 
## [5,] "Bacteria" "Firmicutes"   "Clostridia"  "Oscillospirales"
## [6,] "Bacteria" "Firmicutes"   "Clostridia"  "Lachnospirales" 
##      Family            Genus              Species         
## [1,] "Bacteroidaceae"  "Bacteroides"      "vulgatus"      
## [2,] "Lachnospiraceae" "Blautia"          NA              
## [3,] "Ruminococcaceae" "Faecalibacterium" NA              
## [4,] "Lachnospiraceae" "Agathobacter"     NA              
## [5,] "Ruminococcaceae" "Faecalibacterium" "prausnitzii"   
## [6,] "Lachnospiraceae" "Fusicatenibacter" "saccharivorans"

table(taxa.print[,1], useNA="ifany") # Show the different kingdoms (should be only bacteria)

## 
##  Archaea Bacteria 
##        3     1526

# table(taxa.print[taxa.print[,1] == 'Eukaryota',2], useNA="ifany") # the eukaryota are all NAs

table(taxa.print[,2], useNA="ifany") # Show the different phyla

## 
##   Acidobacteriota  Actinobacteriota      Bacteroidota  Campylobacterota 
##                 2                75               203                 7 
##     Cyanobacteria  Desulfobacterota     Euryarchaeota    Fibrobacterota 
##                21                13                 1                 1 
##        Firmicutes    Fusobacteriota   Patescibacteria    Proteobacteria 
##              1045                22                12               109 
##     Spirochaetota      Synergistota  Thermoplasmatota      Thermotogota 
##                 4                 3                 2                 1 
## Verrucomicrobiota 
##                 8

table(is.na(taxa.print[,2])) # is there any NA phyla?

## 
## FALSE 
##  1529

---

7. LAST PREPROCESSING

---

We will remove any sample with less than 500 reads from further analysis, and also any ASVs with unassigned phyla.

7.1. Create phyloseq object

The preprocessing will be easier to do with ASV, taxonomic and metadata tables combined in a phyloseq object.

#_________________________
# Import metadata
metadata_table <- read.csv(file.path(path.data, "00_Metadata-Mars/modif_metadata(R).csv"), row.names=1)
head(metadata_table)
rownames(metadata_table) <- metadata_table$Run

# double check there isn't ".fastq.gz" from rownames
rownames(seqtable.nochim)[1:5]

#_________________________
# Create phyloseq object
physeq <- phyloseq(otu_table(seqtable.nochim, taxa_are_rows=FALSE), # by default, in otu_table the sequence variants are in rows
                  sample_data(metadata_table), 
                  tax_table(taxa))

# Remove taxa that are eukaryota, or have unassigned Phyla
physeq <- subset_taxa(physeq, Kingdom != "Eukaryota")
physeq <- subset_taxa(physeq, !is.na(Kingdom))
physeq <- subset_taxa(physeq, !is.na(Phylum))
# Remove samples with less than 500 reads
physeq <- prune_samples(sample_sums(physeq)>=500, physeq) # 3 samples got deleted
# Some taxa might have been present only in these low-count samples, so we will make sure to remove taxa that are absent in all samples
physeq <- prune_taxa(taxa_sums(physeq)>0, physeq) # 1524 ASVs final

7.2. Quick peek at data analysis

# Absolute abundance
# plot_bar(physeq, fill = "Phylum")+ facet_wrap("host_disease", scales="free_x") + theme(axis.text.x = element_blank())

# Relative abundance for Phylum
phylum.table <- physeq %>%
  tax_glom(taxrank = "Phylum") %>%                     # agglomerate at phylum level
  transform_sample_counts(function(x) {x/sum(x)} ) %>% # Transform to rel. abundance
  psmelt()                                             # Melt to long format

ggplot(phylum.table, aes(x = Sample, y = Abundance, fill = Phylum))+
  facet_wrap(~ host_disease, scales = "free") + # scales = "free" removes empty lines
  geom_bar(stat = "identity") +
  theme(axis.text.x = element_text(size = 5, angle = -90))+
  labs(x = "Samples", y = "Relative abundance")

7.3. Save to disk

# Save to disk
saveRDS(raw_stats, file.path(path.data, "01_Dada2-Mars/raw_stats.rds"))
saveRDS(FWDpos,    file.path(path.data, "01_Dada2-Mars/forwardreads_primerposition.rds"))
saveRDS(REVpos,    file.path(path.data, "01_Dada2-Mars/reversereads_primerposition.rds"))
saveRDS(FWD.out1,  file.path(path.data, "01_Dada2-Mars/FWD_out1.rds"))
saveRDS(REV.out1,  file.path(path.data, "01_Dada2-Mars/REV_out1.rds"))
saveRDS(out2,      file.path(path.data, "01_Dada2-Mars/out2.rds"))
saveRDS(errF,      file.path(path.data, "01_Dada2-Mars/errF.rds"))
saveRDS(errR,      file.path(path.data, "01_Dada2-Mars/errR.rds"))
saveRDS(dadaFs,    file.path(path.data, "01_Dada2-Mars/infered_seq_F.rds"))
saveRDS(dadaRs,    file.path(path.data, "01_Dada2-Mars/infered_seq_R.rds"))
saveRDS(mergers,   file.path(path.data, "01_Dada2-Mars/mergers.rds"))

# Taxa & Phyloseq object
saveRDS(taxa,   file.path(path.data, "01_Dada2-Mars/taxa_mars.rds"))
saveRDS(physeq, file.path(path.root, "data/analysis-individual/CLUSTER/PhyloTree/input/physeq_mars.rds"))
saveRDS(physeq, file.path(path.root, "data/phyloseq-objects/phyloseq-without-phylotree/physeq_mars.rds"))

---

8. SESSION INFO

---

sessionInfo()

## R version 4.1.3 (2022-03-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur/Monterey 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] stringr_1.5.0               qckitfastq_1.10.0          
##  [3] dplyr_1.1.1                 plyr_1.8.8                 
##  [5] data.table_1.14.8           ggplot2_3.4.2              
##  [7] phyloseq_1.38.0             seqTools_1.28.0            
##  [9] zlibbioc_1.40.0             ShortRead_1.52.0           
## [11] GenomicAlignments_1.30.0    SummarizedExperiment_1.24.0
## [13] Biobase_2.54.0              MatrixGenerics_1.6.0       
## [15] matrixStats_0.63.0          Rsamtools_2.10.0           
## [17] GenomicRanges_1.46.1        BiocParallel_1.28.3        
## [19] Biostrings_2.62.0           GenomeInfoDb_1.30.1        
## [21] XVector_0.34.0              IRanges_2.28.0             
## [23] S4Vectors_0.32.4            BiocGenerics_0.40.0        
## [25] dada2_1.22.0                Rcpp_1.0.10                
## 
## loaded via a namespace (and not attached):
##  [1] nlme_3.1-162           bitops_1.0-7           RColorBrewer_1.1-3    
##  [4] tools_4.1.3            bslib_0.4.2            utf8_1.2.3            
##  [7] R6_2.5.1               vegan_2.6-4            mgcv_1.8-42           
## [10] DBI_1.1.3              colorspace_2.1-0       permute_0.9-7         
## [13] rhdf5filters_1.6.0     ade4_1.7-22            withr_2.5.0           
## [16] tidyselect_1.2.0       compiler_4.1.3         cli_3.6.1             
## [19] DelayedArray_0.20.0    labeling_0.4.2         sass_0.4.5            
## [22] scales_1.2.1           RSeqAn_1.14.0          digest_0.6.31         
## [25] rmarkdown_2.21         jpeg_0.1-10            pkgconfig_2.0.3       
## [28] htmltools_0.5.5        highr_0.10             fastmap_1.1.1         
## [31] rlang_1.1.0            rstudioapi_0.14        farver_2.1.1          
## [34] jquerylib_0.1.4        generics_0.1.3         hwriter_1.3.2.1       
## [37] jsonlite_1.8.4         RCurl_1.98-1.12        magrittr_2.0.3        
## [40] GenomeInfoDbData_1.2.7 biomformat_1.22.0      interp_1.1-4          
## [43] Matrix_1.5-1           munsell_0.5.0          Rhdf5lib_1.16.0       
## [46] fansi_1.0.4            ape_5.7-1              lifecycle_1.0.3       
## [49] stringi_1.7.12         yaml_2.3.7             MASS_7.3-58.3         
## [52] rhdf5_2.38.1           grid_4.1.3             parallel_4.1.3        
## [55] crayon_1.5.2           deldir_1.0-6           lattice_0.20-45       
## [58] splines_4.1.3          multtest_2.50.0        knitr_1.42            
## [61] pillar_1.9.0           igraph_1.4.2           reshape2_1.4.4        
## [64] codetools_0.2-19       glue_1.6.2             evaluate_0.20         
## [67] latticeExtra_0.6-30    RcppParallel_5.1.7     png_0.1-8             
## [70] vctrs_0.6.1            foreach_1.5.2          gtable_0.3.3          
## [73] cachem_1.0.7           xfun_0.38              survival_3.5-5        
## [76] tibble_3.2.1           iterators_1.0.14       cluster_2.1.4